This is a Dataset of the World Population Consisting of Each and Every Country. I have attempted to analyze the same data to bring some insights out of it. The dataset consists of 234 rows and 17 columns. I will analyze the same data and bring the below pieces of information regarding the same.

1. Continent Population Characteristics Analysis.
2. Analysis of Countries.
   • Top 10 Most Populated and Least Populated Countries
   • Top 10 Largest and Smallest Countries as per Area
   • Population Growth From 1970 to 2020 (50 Years)
3. Countries Represent % Of World Population.
   • Countries that represent below 0.1% of the World Population.
   • Countries that represent above 2% of the world Population
   • Top 10 Over Populated Countries based on Density Per Sq KM.
   • Top 10 Least Populated Countries based on Density Per Sq KM.

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

Country: Name of the country.

Density (P/Km2): Population density measured in persons per square kilometer.

Abbreviation: Abbreviation or code representing the country.

Agricultural Land (%): Percentage of land area used for agricultural purposes.

Land Area (Km2): Total land area of the country in square kilometers.

Armed Forces Size: Size of the armed forces in the country.

Birth Rate: Number of births per 1,000 population per year.

Calling Code: International calling code for the country.

Capital/Major City: Name of the capital or major city.

CO2 Emissions: Carbon dioxide emissions in tons.

CPI: Consumer Price Index, a measure of inflation and purchasing power.

CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.

Currency_Code: Currency code used in the country.

Fertility Rate: Average number of children born to a woman during her lifetime.

Forested Area (%): Percentage of land area covered by forests.

Gasoline_Price: Price of gasoline per liter in local currency.

GDP: Gross Domestic Product, the total value of goods and services produced in the country.

Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.

Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.

Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.

Largest City: Name of the country's largest city.

Life Expectancy: Average number of years a newborn is expected to live.

Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.

Minimum Wage: Minimum wage level in local currency.

Official Language: Official language(s) spoken in the country.

Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.

Physicians per Thousand: Number of physicians per thousand people.

Population: Total population of the country.

Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.

Tax Revenue (%): Tax revenue as a percentage of GDP.

Total Tax Rate: Overall tax burden as a percentage of commercial profits.

Unemployment Rate: Percentage of the labor force that is unemployed.

Urban Population: Percentage of the population living in urban areas.

Latitude: Latitude coordinate of the country's location.

Longitude: Longitude coordinate of the country's location.

Potential Use Cases

Analyze population density and land area to study spatial distribution patterns.

Investigate the relationship between agricultural land and food security.

Examine carbon dioxide emissions and their impact on climate change.

Explore correlations between economic indicators such as GDP and various socio-economic factors.

Investigate educational enrollment rates and their implications for human capital development.

Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.

Study labor market dynamics through indicators such as labor force participation and unemployment rates.

Investigate the role of taxation and its impact on economic development.

Explore urbanization trends and their social and environmental consequences.

Context

The United States Census Bureau’s international dataset provides estimates of country populations since 1950 and projections through 2050. Specifically, the dataset includes midyear population figures broken down by age and gender assignment at birth. Additionally, time-series data is provided for attributes including fertility rates, birth rates, death rates, and migration rates.

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.census_bureau_international.

Sample Query 1

What countries have the longest life expectancy? In this query, 2016 census information is retrieved by joining the mortality_life_expectancy and country_names_area tables for countries larger than 25,000 km2. Without the size constraint, Monaco is the top result with an average life expectancy of over 89 years!

standardSQL

SELECT age.country_name, age.life_expectancy, size.country_area FROM ( SELECT country_name, life_expectancy FROM bigquery-public-data.census_bureau_international.mortality_life_expectancy WHERE year = 2016) age INNER JOIN ( SELECT country_name, country_area FROM bigquery-public-data.census_bureau_international.country_names_area where country_area > 25000) size ON age.country_name = size.country_name ORDER BY 2 DESC /* Limit removed for Data Studio Visualization */ LIMIT 10

Sample Query 2

Which countries have the largest proportion of their population under 25? Over 40% of the world’s population is under 25 and greater than 50% of the world’s population is under 30! This query retrieves the countries with the largest proportion of young people by joining the age-specific population table with the midyear (total) population table.

standardSQL

SELECT age.country_name, SUM(age.population) AS under_25, pop.midyear_population AS total, ROUND((SUM(age.population) / pop.midyear_population) * 100,2) AS pct_under_25 FROM ( SELECT country_name, population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population_agespecific WHERE year =2017 AND age < 25) age INNER JOIN ( SELECT midyear_population, country_code FROM bigquery-public-data.census_bureau_international.midyear_population WHERE year = 2017) pop ON age.country_code = pop.country_code GROUP BY 1, 3 ORDER BY 4 DESC /* Remove limit for visualization*/ LIMIT 10

Sample Query 3

The International Census dataset contains growth information in the form of birth rates, death rates, and migration rates. Net migration is the net number of migrants per 1,000 population, an important component of total population and one that often drives the work of the United Nations Refugee Agency. This query joins the growth rate table with the area table to retrieve 2017 data for countries greater than 500 km2.

SELECT growth.country_name, growth.net_migration, CAST(area.country_area AS INT64) AS country_area FROM ( SELECT country_name, net_migration, country_code FROM bigquery-public-data.census_bureau_international.birth_death_growth_rates WHERE year = 2017) growth INNER JOIN ( SELECT country_area, country_code FROM bigquery-public-data.census_bureau_international.country_names_area

Update frequency

Historic (none)

Dataset source

United States Census Bureau

Terms of use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

See the GCP Marketplace listing for more details and sample queries: https://console.cloud.google.com/marketplace/details/united-states-census-bureau/international-census-data

Saudi Arabia SA: Population in Largest City data was reported at 6,669,098.000 Person in 2017. This records an increase from the previous number of 6,439,767.000 Person for 2016. Saudi Arabia SA: Population in Largest City data is updated yearly, averaging 2,066,960.500 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 6,669,098.000 Person in 2017 and a record low of 156,699.000 Person in 1960. Saudi Arabia SA: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Saudi Arabia – Table SA.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;

This dataset provides values for POPULATION reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Even though Canada is the second largest country in the world in terms of land area, it ranks 33rd in terms of population. Almost all of Canada’s population is concentrated in a narrow band along the country’s southern edge. Nearly 80% of the total population lives within the 25 major metropolitan areas, which represent only 0.79% of the total area of the country.

This dataset provides key economic indicators for five of the world's largest economies, based on their nominal Gross Domestic Product (GDP) in 2022. It includes the GDP values, population, GDP growth rates, per capita GDP, and each country's share of the global economy.

Columns: Country: Name of the country. GDP (nominal, 2022): The total nominal GDP in 2022, represented in USD. GDP (abbrev.): The abbreviated GDP in trillions of USD. GDP growth: The percentage growth in GDP compared to the previous year. Population: Total population of each country in 2022. GDP per capita: The GDP per capita, representing average economic output per person in USD. Share of world GDP: The percentage of global GDP contributed by each country. Key Highlights: The dataset includes some of the largest global economies, such as the United States, China, Japan, Germany, and India. The data can be used to analyze the economic standing of countries in terms of overall GDP and per capita wealth. It offers insights into the relative growth rates and population sizes of these leading economies. This dataset is ideal for exploring economic trends, performing country-wise comparisons, or studying the relationship between population size and GDP growth.

The West Africa Coastal Vulnerability Mapping: Population Projections, 2030 and 2050 data set is based on an unreleased working version of the Gridded Population of the World (GPW), Version 4, year 2010 population count raster but at a coarser 5 arc-minute resolution. Bryan Jones of Baruch College produced country-level projections based on the Shared Socioeconomic Pathway 4 (SSP4). SSP4 reflects a divided world where cities that have relatively high standards of living, are attractive to internal and international migrants. In low income countries, rapidly growing rural populations live on shrinking areas of arable land due to both high population pressure and expansion of large-scale mechanized farming by international agricultural firms. This pressure induces large migration flow to the cities, contributing to fast urbanization, although urban areas do not provide many opportUnities for the poor and there is a massive expansion of slums and squatter settlements. This scenario may not be the most likely for the West Africa region, but it has internal coherence and is at least plausible.

Associated with manuscript titled: Fifty Muslim-majority countries have fewer COVID-19 cases and deaths than the 50 richest non-Muslim countriesThe objective of this research was to determine the difference in the total number of COVID-19 cases and deaths between Muslim-majority and non-Muslim countries, and investigate reasons for the disparities. Methods: The 50 Muslim-majority countries had more than 50.0% Muslims with an average of 87.5%. The non-Muslim country sample consisted of 50 countries with the highest GDP while omitting any Muslim-majority countries listed. The non-Muslim countries’ average percentage of Muslims was 4.7%. Data pulled on September 18, 2020 included the percentage of Muslim population per country by World Population Review15 and GDP per country, population count, and total number of COVID-19 cases and deaths by Worldometers.16 The data set was transferred via an Excel spreadsheet on September 23, 2020 and analyzed. To measure COVID-19’s incidence in the countries, three different Average Treatment Methods (ATE) were used to validate the results. Results published as a preprint at https://doi.org/10.31235/osf.io/84zq5(15) Muslim Majority Countries 2020 [Internet]. Walnut (CA): World Population Review. 2020- [Cited 2020 Sept 28]. Available from: http://worldpopulationreview.com/country-rankings/muslim-majority-countries (16) Worldometers.info. Worldometer. Dover (DE): Worldometer; 2020 [cited 2020 Sept 28]. Available from: http://worldometers.info

This dataset shows where media and press are most free to express their views and opinions. Countries rankings are based on laws, violence, and deaths of reporters and journalists. This is a Different measure of freedom than the world freedom index but just as important. This dataset shows the availability of dissenting views and opinions allowed within a Country. The USA was surprisingly ranked 44th, where freedom of speech is supposed to be one of out most prized rights. 1st place was a tie between northern European Countries (Denmark, Finland, Switzerland, Ireland, Iceland, Norway, and the Netherlands) Source URL: http://www.worldpress.org/link.cfm?http://www.rsf.org/article.php3?id_article=15333

this graph was created in R:

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driven primarily by high birth rates in developing countries and advancements in healthcare. According to the United Nations, the global population surpassed 8 billion in 2023, marking a critical milestone in human history. This growth, however, is unevenly distributed across continents and countries, leading to varied population densities and urban pressures.

Surface area and population density play vital roles in shaping the demographic and economic landscape of each country. For instance, countries with large land masses such as Russia, Canada, and Australia have low population densities despite their significant populations, as vast portions of their land are sparsely populated or uninhabitable. Conversely, nations like Bangladesh and South Korea exhibit extremely high population densities due to smaller land areas combined with large populations.

Population density, measured as the number of people per square kilometer, affects resource availability, environmental sustainability, and quality of life. High-density areas face greater challenges in housing, infrastructure, and environmental management, often experiencing increased pollution and resource strain. In contrast, low-density areas may struggle with underdeveloped infrastructure and limited access to services due to the dispersed population.

Urbanization trends are another important aspect of these dynamics. As people migrate to cities seeking better economic opportunities, urban areas grow more densely populated, amplifying the need for efficient land use and sustainable urban planning. The UN reports that over half of the world’s population currently resides in urban areas, with this figure expected to rise to nearly 70% by 2050. This shift requires nations to balance population growth and density with sustainable development strategies to ensure a higher quality of life and environmental stewardship for future generations.

Through an understanding of population size, surface area, and density, policymakers can better address challenges related to urban development, rural depopulation, and resource allocation, supporting a balanced approach to population management and economic development.

Australia Population in Largest City: as % of Urban Population data was reported at 22.768 % in 2024. This records an increase from the previous number of 22.673 % for 2023. Australia Population in Largest City: as % of Urban Population data is updated yearly, averaging 24.964 % from Dec 1960 (Median) to 2024, with 65 observations. The data reached an all-time high of 27.701 % in 1971 and a record low of 22.181 % in 2013. Australia Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Australia – Table AU.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.;United Nations, World Urbanization Prospects.;Weighted average;

This dataset displays countries that had ten percent or more of their population age 65 and older. This data was collecte through agingstats.gov.

Street Noise-Level Dataset

Silencio’s Street Noise-Level Dataset offers unique access to hyper-local, real-world noise exposure data across more than 180 countries. Built from over 35 billion datapoints, collected via our mobile app and enriched with AI-powered interpolation, this dataset delivers detailed average noise levels (dBA) at the street and neighborhood level.

Chronic noise exposure is a growing public health concern linked to stress, cardiovascular risks, sleep disorders, and reduced quality of life — all of which are increasingly relevant for public health studies, insurance risk modeling, and wellness program design. Silencio’s data allows buyers to quantify environmental noise exposure and incorporate it into risk assessments, premium modeling, urban health studies, and wellness product development.

In addition to objective noise measurements, Silencio provides access to the world’s largest noise complaint database, offering complementary subjective insights directly from communities, enabling more precise correlations between noise exposure and health outcomes.

Data is available as: • CSV exports • S3 bucket delivery • High-resolution maps, perfect for health impact assessments, research publications, or integration into insurance models.

We provide both historical and real-time data. An API is currently in development, and we welcome custom requests and early access partnerships.

Fully anonymized and GDPR-compliant, our dataset is ready to enhance health-focused research, insurance underwriting, and product innovation.

This dataset provides values for EMPLOYMENT RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

This dataset displays the number of television receivers by country for the time period covering 1990 through 1997. Covered throughout this dataset is 150+ countries, This dataset was gathered from the United Nations Statistics Division. http://unstats.un.org/unsd/databases.htm Access Date: October 31, 2007

This is the World Freedom index, By the State of World Liberty Project. It ranks various countries by various forms of freedom and created an index to see which countries had the most freedom. USA finished 8th, with Estonia in 1st place and North Korea having the least freedom. Source URL: http://www.stateofworldliberty.org/report/rankings.html This Dataset has a ranking for the countries, just to be clear, when you map out the rankings of countries, the highest ranked countries will not be the brightest on the map. Estonia is Ranked #1, but the value of 1 is lower than the value assigned to North Korea (158). so just be aware of that. In short, for mapping, use the Scores not the Ranks.

The 2007 World Bank Group Entrepreneurship Survey measures entrepreneurial activity in 84 developing and industrial countries over the period 2003-2005. The database includes cross-country, time-series data on the number of total and newly registered businesses, collected directly from Registrar of Companies around the world. In its second year, this survey incorporates improvements in methodology, and expanded participation from countries covered, allowing for greater cross-border compatibility of data compared with the 2006 survey. This joint effort by the IFC SME Department and the World Bank Developing Research Group is the most comprehensive dataset on cross-country firm entry data available today. This database The World Bank Group Entrepreneurship Dataaset presents data collected primarily from country business registries using the first annual World Bank Group Questionnaire on Entrepreneurship (alternative sources were tax authorities, finance ministries, and national statistics offices). For more information on the author of the database, Leora Klapper, visit: http://go.worldbank.org/DK5AHCQSO0. This data was access at the preceeding link, on October 11, 2007. Please visit the link for more information in regards to this dataset.

This dataset illustrates the largest difference between high and low temperatures and the smallest difference between high and low temperatures in cities with 50,000 people or more. A value of -1 means that the data was not applicable. Also included are the rankings, the inverse ranking to be used for mapping purposes, the popualtion, the name of city and state, and the temperature degree difference. Source City-Data URL http//www.city-data.com/top2/c489.html http//www.city-data.com/top2/c490.html Date Accessed November 13,2007

This dataset displays the annual beef and veal exports by country from the United States. This is on a Carcass wt. 1000 pound scale. The time period covered stretches from 2003 to January of 2008.